Partitioning the nodes of a graph
Graph theory with applications to algorithms and computer science
Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Spectral partitioning: the more eigenvectors, the better
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
A gradient method on the initial partition of Fiduccia-Mattheyses algorithm
ICCAD '95 Proceedings of the 1995 IEEE/ACM international conference on Computer-aided design
VLSI circuit partitioning by cluster-removal using iterative improvement techniques
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Multi-level spectral hypergraph partitioning with arbitrary vertex sizes
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Multilevel circuit partitioning
DAC '97 Proceedings of the 34th annual Design Automation Conference
An evaluation of bipartitioning techniques
ARVLSI '95 Proceedings of the 16th Conference on Advanced Research in VLSI (ARVLSI'95)
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
HYBRID SPECTRAL/ITERATIVE PARTITIONING
HYBRID SPECTRAL/ITERATIVE PARTITIONING
Spectral-based multiway FPGA partitioning
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Multiway partitioning via geometric embeddings, orderings, and dynamic programming
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
| Hi-index | 0.00 |
We develop a new multi-way, hybrid spectral/iterative hypergraph partitioning algorithm that combines the strengths of spectral partitioners and iterative improvement algorithms to create a new class of partitioners. We use spectral information (the eigenvectors of a graph) to generate initial partitions, influence the selection of iterative improvement moves, and break out of local minima. Our 3-way and 4-way partitioning results exhibit significant improvement over current published results, demonstrating the effectiveness of our new method. Our hybrid algorithm produces an improvement of 25% over GFM for 3-way partitions, 41% improvement over GFM for 4-way partitions, and 58% improvement over ML_F for 4-way partitions.